Efficient Automated Glacier Surface Velocity Measurement From Repeat Images Using Multi-Image/Multichip and Null Exclusion Feature Tracking

Abstract

Observations of ice motion are critical for constraining ice sheet mass balance and contribution to sea level rise, as well as predicting future changes. A wealth of imagery now exists for measuring ice motion from space, but existing repeat-image feature-tracking (RIFT) algorithms require the selection of several location- and data-specific parameters and manual data editing and are therefore not efficient for processing large numbers of image pairs for differing regions. Here, we present the multiple-image/multiple-chip RIFT algorithm which does not involve any user-defined local/empirical parameters and has a higher matching success rate than conventional single-image single-chip correlation matching. We also present an efficient method for applying RIFT to null-value striped data, such as the Landsat-7 Enhanced Thematic Mapper Plus. This method offers the potential for fully automated processing of large data sets.